"""simple docstring"""


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


a				=getLogger(__name__)


a				='cuda' if torch.cuda.is_available() else 'cpu'





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 8  ,       __lowerCAmelCase = DEFAULT_DEVICE  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase="summarization"  ,       __lowerCAmelCase=None  ,       **__lowerCAmelCase  ,       )				->   Dict:




						'''simple docstring'''



						lowerCamelCase__   =Path(__lowerCAmelCase							).open("w"  ,       encoding="utf-8"							)
						lowerCamelCase__   =str(__lowerCAmelCase							)
						lowerCamelCase__   =AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)
						if fpaa:
												lowerCamelCase__   =model.half()

						lowerCamelCase__   =AutoTokenizer.from_pretrained(__lowerCAmelCase							)
						logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''							)  # if this is wrong, check config.model_type.

						lowerCamelCase__   =time.time()
						# update config with task specific params
						use_task_specific_params(__lowerCAmelCase  ,       __lowerCAmelCase							)
						if prefix is None:
												lowerCamelCase__   =prefix or getattr(model.config  ,       "prefix"  ,       ""							) or ""
						for examples_chunk in tqdm(list(chunks(__lowerCAmelCase  ,       __lowerCAmelCase							)							)							):
												lowerCamelCase__   =[prefix + text for text in examples_chunk]
												lowerCamelCase__   =tokenizer(__lowerCAmelCase  ,       return_tensors="pt"  ,       truncation=__lowerCAmelCase  ,       padding="longest"							).to(__lowerCAmelCase							)
												lowerCamelCase__   =model.generate(
												    input_ids=batch.input_ids  ,       attention_mask=batch.attention_mask  ,       **__lowerCAmelCase  ,       )
												lowerCamelCase__   =tokenizer.batch_decode(__lowerCAmelCase  ,       skip_special_tokens=__lowerCAmelCase  ,       clean_up_tokenization_spaces=__lowerCAmelCase							)
												for hypothesis in dec:
																		fout.write(hypothesis + "\n"							)
																		fout.flush()
						fout.close()
						lowerCamelCase__   =int(time.time() - start_time							)  # seconds
						lowerCamelCase__   =len(__lowerCAmelCase							)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs  ,       4							)}





def      lowerCamelCase_					(   )				->   Optional[int]:




						'''simple docstring'''



						return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"							)





def      lowerCamelCase_					(   __lowerCAmelCase=True							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =argparse.ArgumentParser()
						parser.add_argument("model_name"  ,       type=__lowerCAmelCase  ,       help="like facebook/bart-large-cnn,t5-base, etc."							)
						parser.add_argument("input_path"  ,       type=__lowerCAmelCase  ,       help="like cnn_dm/test.source"							)
						parser.add_argument("save_path"  ,       type=__lowerCAmelCase  ,       help="where to save summaries"							)
						parser.add_argument("--reference_path"  ,       type=__lowerCAmelCase  ,       required=__lowerCAmelCase  ,       help="like cnn_dm/test.target"							)
						parser.add_argument("--score_path"  ,       type=__lowerCAmelCase  ,       required=__lowerCAmelCase  ,       default="metrics.json"  ,       help="where to save metrics"							)
						parser.add_argument("--device"  ,       type=__lowerCAmelCase  ,       required=__lowerCAmelCase  ,       default=__lowerCAmelCase  ,       help="cuda, cuda:1, cpu etc."							)
						parser.add_argument(
						    "--prefix"  ,       type=__lowerCAmelCase  ,       required=__lowerCAmelCase  ,       default=__lowerCAmelCase  ,       help="will be added to the begininng of src examples"							)
						parser.add_argument("--task"  ,       type=__lowerCAmelCase  ,       default="summarization"  ,       help="used for task_specific_params + metrics"							)
						parser.add_argument("--bs"  ,       type=__lowerCAmelCase  ,       default=8  ,       required=__lowerCAmelCase  ,       help="batch size"							)
						parser.add_argument(
						    "--n_obs"  ,       type=__lowerCAmelCase  ,       default=-1  ,       required=__lowerCAmelCase  ,       help="How many observations. Defaults to all."							)
						parser.add_argument("--fp16"  ,       action="store_true"							)
						parser.add_argument("--dump-args"  ,       action="store_true"  ,       help="print the custom hparams with the results"							)
						parser.add_argument(
						    "--info"  ,       nargs="?"  ,       type=__lowerCAmelCase  ,       const=datetime_now()  ,       help=(
						        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
						        " lang=en-ru. If no value is passed, the current datetime string will be used."
						    )  ,       )
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						lowerCamelCase__   , lowerCamelCase__   =parser.parse_known_args()
						lowerCamelCase__   =parse_numeric_n_bool_cl_kwargs(__lowerCAmelCase							)
						if parsed_args and verbose:
												print(F'''parsed the following generate kwargs: {parsed_args}'''							)
						lowerCamelCase__   =[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
						if args.n_obs > 0:
												lowerCamelCase__   =examples[: args.n_obs]
						Path(args.save_path							).parent.mkdir(exist_ok=__lowerCAmelCase							)

						if args.reference_path is None and Path(args.score_path							).exists():
												warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''							)

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError("Can't mix --fp16 and --device cpu"							)

						lowerCamelCase__   =generate_summaries_or_translations(
						    __lowerCAmelCase  ,       args.save_path  ,       args.model_name  ,       batch_size=args.bs  ,       device=args.device  ,       fpaa=args.fpaa  ,       task=args.task  ,       prefix=args.prefix  ,       **__lowerCAmelCase  ,       )

						if args.reference_path is None:
												return {}

						# Compute scores
						lowerCamelCase__   =calculate_bleu if "translation" in args.task else calculate_rouge
						lowerCamelCase__   =[x.rstrip() for x in open(args.save_path							).readlines()]
						lowerCamelCase__   =[x.rstrip() for x in open(args.reference_path							).readlines()][: len(__lowerCAmelCase							)]
						lowerCamelCase__   =score_fn(__lowerCAmelCase  ,       __lowerCAmelCase							)
						scores.update(__lowerCAmelCase							)

						if args.dump_args:
												scores.update(__lowerCAmelCase							)
						if args.info:
												lowerCamelCase__   =args.info

						if verbose:
												print(__lowerCAmelCase							)

						if args.score_path is not None:
												json.dump(__lowerCAmelCase  ,       open(args.score_path  ,       "w"							)							)

						return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)



"""simple docstring"""


from __future__ import annotations

from typing import Any






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 0 ):
										lowerCamelCase__   , lowerCamelCase__   =row, column
										lowerCamelCase__   =[[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]

				def __str__( self ):
										lowerCamelCase__   =F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCamelCase__   =0
										for row_vector in self.array:
																for obj in row_vector:
																						lowerCamelCase__   =max(_lowerCamelCase      ,					len(str(_lowerCamelCase ) ) )
										lowerCamelCase__   =F'''%{max_element_length}s'''

										# Make string and return
										def single_line(_lowerCamelCase ) -> str:
																nonlocal string_format_identifier
																lowerCamelCase__   ="["
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																line += "]"
																return line

										s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
										return s

				def __repr__( self ):
										return str(self )

				def 						_a      ( self      ,					_lowerCamelCase ):
										if not (isinstance(_lowerCamelCase      ,					(list, tuple) ) and len(_lowerCamelCase ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__( self      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										return self.array[loc[0]][loc[1]]

				def __setitem__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										lowerCamelCase__   =value

				def __add__( self      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == another.row and self.column == another.column

										# Add
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c] + another[r, c]
										return result

				def __neg__( self ):
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =-self[r, c]
										return result

				def __sub__( self      ,					_lowerCamelCase ):
										return self + (-another)

				def __mul__( self      ,					_lowerCamelCase ):

										if isinstance(_lowerCamelCase      ,					(int, float) ):  # Scalar multiplication
																lowerCamelCase__   =Matrix(self.row      ,					self.column )
																for r in range(self.row ):
																						for c in range(self.column ):
																												lowerCamelCase__   =self[r, c] * another
																return result
										elif isinstance(_lowerCamelCase      ,					_lowerCamelCase ):  # Matrix multiplication
																assert self.column == another.row
																lowerCamelCase__   =Matrix(self.row      ,					another.column )
																for r in range(self.row ):
																						for c in range(another.column ):
																												for i in range(self.column ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																lowerCamelCase__   =F'''Unsupported type given for another ({type(_lowerCamelCase )})'''
																raise TypeError(_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =Matrix(self.column      ,					self.row )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c]
										return result






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase ) and isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										lowerCamelCase__   =v.transpose()
										lowerCamelCase__   =(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							lowerCamelCase__   =Matrix(3  ,       3  ,       0							)
							for i in range(3							):
													lowerCamelCase__   =1
							print(F'''a^(-1) is {ainv}'''							)
							# u, v
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =1, 2, -3
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =4, -2, 5
							print(F'''u is {u}'''							)
							print(F'''v is {v}'''							)
							print(F'''uv^T is {u * v.transpose()}'''							)
							# Sherman Morrison
							print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase  ,       __lowerCAmelCase							)}'''							)





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							import doctest

							doctest.testmod()





	testa()


